
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=1    )	->						List[Any]:
				if n_shave_prefix_segments >= 0:
								return ".".join(path.split("""."""    )[n_shave_prefix_segments:]    )
				else:
								return ".".join(path.split("""."""    )[:n_shave_prefix_segments]    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=0    )	->						Any:
				UpperCamelCase_:				Any						=    []
				for old_item in old_list:
								UpperCamelCase_:				Union[str, Any]						=    old_item.replace("""in_layers.0"""  ,     """norm1"""    )
								UpperCamelCase_:				Optional[int]						=    new_item.replace("""in_layers.2"""  ,     """conv1"""    )

								UpperCamelCase_:				List[str]						=    new_item.replace("""out_layers.0"""  ,     """norm2"""    )
								UpperCamelCase_:				List[str]						=    new_item.replace("""out_layers.3"""  ,     """conv2"""    )

								UpperCamelCase_:				Optional[Any]						=    new_item.replace("""emb_layers.1"""  ,     """time_emb_proj"""    )
								UpperCamelCase_:				List[str]						=    new_item.replace("""skip_connection"""  ,     """conv_shortcut"""    )

								UpperCamelCase_:				List[str]						=    shave_segments(lowerCamelCase  ,     n_shave_prefix_segments=lowerCamelCase    )

								mapping.append({"""old""": old_item, """new""": new_item}    )

				return mapping







def 							A__       (    lowerCamelCase  ,     lowerCamelCase=0    )	->						Dict:
				UpperCamelCase_:				List[str]						=    []
				for old_item in old_list:
								UpperCamelCase_:				Union[str, Any]						=    old_item

								UpperCamelCase_:				Optional[Any]						=    new_item.replace("""norm.weight"""  ,     """group_norm.weight"""    )
								UpperCamelCase_:				List[str]						=    new_item.replace("""norm.bias"""  ,     """group_norm.bias"""    )

								UpperCamelCase_:				Any						=    new_item.replace("""proj_out.weight"""  ,     """proj_attn.weight"""    )
								UpperCamelCase_:				Any						=    new_item.replace("""proj_out.bias"""  ,     """proj_attn.bias"""    )

								UpperCamelCase_:				Dict						=    shave_segments(lowerCamelCase  ,     n_shave_prefix_segments=lowerCamelCase    )

								mapping.append({"""old""": old_item, """new""": new_item}    )

				return mapping







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=None  ,     lowerCamelCase=None  ,     lowerCamelCase=None    )	->						Any:
				assert isinstance(lowerCamelCase  ,     lowerCamelCase    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												UpperCamelCase_:				List[Any]						=    old_checkpoint[path]
												UpperCamelCase_:				Optional[int]						=    old_tensor.shape[0] // 3

												UpperCamelCase_:				Any						=    (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)

												UpperCamelCase_:				Dict						=    old_tensor.shape[0] // config["""num_head_channels"""] // 3

												UpperCamelCase_:				List[str]						=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
												UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    old_tensor.split(channels // num_heads  ,     dim=1    )

												UpperCamelCase_:				List[str]						=    query.reshape(lowerCamelCase    )
												UpperCamelCase_:				str						=    key.reshape(lowerCamelCase    )
												UpperCamelCase_:				Optional[int]						=    value.reshape(lowerCamelCase    )

				for path in paths:
								UpperCamelCase_:				List[str]						=    path["""new"""]

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								# Global renaming happens here
								UpperCamelCase_:				Optional[Any]						=    new_path.replace("""middle_block.0"""  ,     """mid_block.resnets.0"""    )
								UpperCamelCase_:				List[str]						=    new_path.replace("""middle_block.1"""  ,     """mid_block.attentions.0"""    )
								UpperCamelCase_:				Tuple						=    new_path.replace("""middle_block.2"""  ,     """mid_block.resnets.1"""    )

								if additional_replacements is not None:
												for replacement in additional_replacements:
																UpperCamelCase_:				Union[str, Any]						=    new_path.replace(replacement["""old"""]  ,     replacement["""new"""]    )

        # proj_attn.weight has to be converted from conv 1D to linear
								if "proj_attn.weight" in new_path:
												UpperCamelCase_:				Tuple						=    old_checkpoint[path["""old"""]][:, :, 0]
								else:
												UpperCamelCase_:				Union[str, Any]						=    old_checkpoint[path["""old"""]]







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						int:
				UpperCamelCase_:				Union[str, Any]						=    {}

				UpperCamelCase_:				int						=    checkpoint["""time_embed.0.weight"""]
				UpperCamelCase_:				List[Any]						=    checkpoint["""time_embed.0.bias"""]
				UpperCamelCase_:				List[Any]						=    checkpoint["""time_embed.2.weight"""]
				UpperCamelCase_:				str						=    checkpoint["""time_embed.2.bias"""]

				UpperCamelCase_:				Union[str, Any]						=    checkpoint["""input_blocks.0.0.weight"""]
				UpperCamelCase_:				Optional[int]						=    checkpoint["""input_blocks.0.0.bias"""]

				UpperCamelCase_:				List[str]						=    checkpoint["""out.0.weight"""]
				UpperCamelCase_:				Tuple						=    checkpoint["""out.0.bias"""]
				UpperCamelCase_:				Tuple						=    checkpoint["""out.2.weight"""]
				UpperCamelCase_:				Optional[Any]						=    checkpoint["""out.2.bias"""]

				# Retrieves the keys for the input blocks only
				UpperCamelCase_:				Optional[int]						=    len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """input_blocks""" in layer}    )
				UpperCamelCase_:				Optional[Any]						=    {
				    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(lowerCamelCase    )
				}

				# Retrieves the keys for the middle blocks only
				UpperCamelCase_:				str						=    len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """middle_block""" in layer}    )
				UpperCamelCase_:				List[Any]						=    {
				    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
				    for layer_id in range(lowerCamelCase    )
				}

				# Retrieves the keys for the output blocks only
				UpperCamelCase_:				Optional[Any]						=    len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """output_blocks""" in layer}    )
				UpperCamelCase_:				int						=    {
				    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(lowerCamelCase    )
				}

				for i in range(1  ,     lowerCamelCase    ):
								UpperCamelCase_:				List[Any]						=    (i - 1) // (config["""num_res_blocks"""] + 1)
								UpperCamelCase_:				Tuple						=    (i - 1) % (config["""num_res_blocks"""] + 1)

								UpperCamelCase_:				Tuple						=    [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
								UpperCamelCase_:				str						=    [key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]

								if F'''input_blocks.{i}.0.op.weight''' in checkpoint:
												UpperCamelCase_:				Any						=    checkpoint[
												    F'''input_blocks.{i}.0.op.weight'''
												]
												UpperCamelCase_:				Any						=    checkpoint[
												    F'''input_blocks.{i}.0.op.bias'''
												]
												continue

								UpperCamelCase_:				List[Any]						=    renew_resnet_paths(lowerCamelCase    )
								UpperCamelCase_:				str						=    {"""old""": F'''input_blocks.{i}.0''', """new""": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								UpperCamelCase_:				Any						=    {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
								assign_to_checkpoint(
								    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     additional_replacements=[meta_path, resnet_op]  ,     config=lowerCamelCase    )

								if len(lowerCamelCase    ):
												UpperCamelCase_:				Optional[int]						=    renew_attention_paths(lowerCamelCase    )
												UpperCamelCase_:				List[str]						=    {
												    """old""": F'''input_blocks.{i}.1''',
												    """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												UpperCamelCase_:				Any						=    {
												    F'''input_blocks.{i}.1.qkv.bias''': {
												        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    F'''input_blocks.{i}.1.qkv.weight''': {
												        """key""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        """query""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        """value""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     additional_replacements=[meta_path]  ,     attention_paths_to_split=lowerCamelCase  ,     config=lowerCamelCase  ,     )

				UpperCamelCase_:				int						=    middle_blocks[0]
				UpperCamelCase_:				int						=    middle_blocks[1]
				UpperCamelCase_:				Union[str, Any]						=    middle_blocks[2]

				UpperCamelCase_:				Tuple						=    renew_resnet_paths(lowerCamelCase    )
				assign_to_checkpoint(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     config=lowerCamelCase    )

				UpperCamelCase_:				Any						=    renew_resnet_paths(lowerCamelCase    )
				assign_to_checkpoint(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     config=lowerCamelCase    )

				UpperCamelCase_:				Tuple						=    renew_attention_paths(lowerCamelCase    )
				UpperCamelCase_:				List[Any]						=    {
				    """middle_block.1.qkv.bias""": {
				        """key""": """mid_block.attentions.0.key.bias""",
				        """query""": """mid_block.attentions.0.query.bias""",
				        """value""": """mid_block.attentions.0.value.bias""",
				    },
				    """middle_block.1.qkv.weight""": {
				        """key""": """mid_block.attentions.0.key.weight""",
				        """query""": """mid_block.attentions.0.query.weight""",
				        """value""": """mid_block.attentions.0.value.weight""",
				    },
				}
				assign_to_checkpoint(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     attention_paths_to_split=lowerCamelCase  ,     config=lowerCamelCase    )

				for i in range(lowerCamelCase    ):
								UpperCamelCase_:				Optional[Any]						=    i // (config["""num_res_blocks"""] + 1)
								UpperCamelCase_:				Dict						=    i % (config["""num_res_blocks"""] + 1)
								UpperCamelCase_:				Dict						=    [shave_segments(lowerCamelCase  ,     2    ) for name in output_blocks[i]]
								UpperCamelCase_:				Optional[Any]						=    {}

								for layer in output_block_layers:
												UpperCamelCase_, UpperCamelCase_:				List[Any]						=    layer.split("""."""    )[0], shave_segments(lowerCamelCase  ,     1    )
												if layer_id in output_block_list:
																output_block_list[layer_id].append(lowerCamelCase    )
												else:
																UpperCamelCase_:				str						=    [layer_name]

								if len(lowerCamelCase    ) > 1:
												UpperCamelCase_:				Optional[Any]						=    [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
												UpperCamelCase_:				Dict						=    [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

												UpperCamelCase_:				Tuple						=    renew_resnet_paths(lowerCamelCase    )
												UpperCamelCase_:				List[str]						=    renew_resnet_paths(lowerCamelCase    )

												UpperCamelCase_:				List[str]						=    {"""old""": F'''output_blocks.{i}.0''', """new""": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     additional_replacements=[meta_path]  ,     config=lowerCamelCase    )

												if ["conv.weight", "conv.bias"] in output_block_list.values():
																UpperCamelCase_:				Optional[Any]						=    list(output_block_list.values()    ).index(["""conv.weight""", """conv.bias"""]    )
																UpperCamelCase_:				Union[str, Any]						=    checkpoint[
																    F'''output_blocks.{i}.{index}.conv.weight'''
																]
																UpperCamelCase_:				Union[str, Any]						=    checkpoint[
																    F'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(lowerCamelCase    ) == 2:
																				UpperCamelCase_:				List[str]						=    []

												if len(lowerCamelCase    ):
																UpperCamelCase_:				int						=    renew_attention_paths(lowerCamelCase    )
																UpperCamelCase_:				Union[str, Any]						=    {
																    """old""": F'''output_blocks.{i}.1''',
																    """new""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																UpperCamelCase_:				Union[str, Any]						=    {
																    F'''output_blocks.{i}.1.qkv.bias''': {
																        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
																        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
																        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
																    },
																    F'''output_blocks.{i}.1.qkv.weight''': {
																        """key""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
																        """query""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
																        """value""": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
																    },
																}
																assign_to_checkpoint(
																    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     additional_replacements=[meta_path]  ,     attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions    ) else None  ,     config=lowerCamelCase  ,     )
								else:
												UpperCamelCase_:				str						=    renew_resnet_paths(lowerCamelCase  ,     n_shave_prefix_segments=1    )
												for path in resnet_0_paths:
																UpperCamelCase_:				Optional[int]						=    """.""".join(["""output_blocks""", str(lowerCamelCase    ), path["""old"""]]    )
																UpperCamelCase_:				Optional[Any]						=    """.""".join(["""up_blocks""", str(lowerCamelCase    ), """resnets""", str(lowerCamelCase    ), path["""new"""]]    )

																UpperCamelCase_:				int						=    checkpoint[old_path]

				return new_checkpoint


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument(
							    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
							)

							parser.add_argument(
							    """--config_file""",
							    default=None,
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the architecture.""",
							)

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							lowerCamelCase_    :			List[Any]    =	parser.parse_args()

							lowerCamelCase_    :			Any    =	torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														lowerCamelCase_    :			int    =	json.loads(f.read())

							lowerCamelCase_    :			str    =	convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							lowerCamelCase_    :			str    =	UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														lowerCamelCase_    :			Optional[Any]    =	DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
														lowerCamelCase_    :			int    =	VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

														lowerCamelCase_    :			List[str]    =	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






